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Paper Number
1499
Paper Type
Completed
Description
Artificial intelligence (AI) is transforming the nature of work and reshaping labor markets. Viewing labor as a bundle of skills, recent research has analyzed AI skills and offered important insights about the impacts of AI on labor markets. We add to this on-going discourse and argue that taking a dynamic skill-based approach to measurement is critical: just like the development of AI is emergent and ever-evolving, so are AI skills. Taking stock of the literature, we show that existing studies tend to take a static approach to measuring AI skills, which fails to fully reflect the dynamic phenomenon of AI skills and could cause measurement errors. We propose a dynamic co-occurrence method and demonstrate that it performs better than the extant static methods, which can cause severe Type I and II errors, omit emerging AI skills, and temporally over- and under-estimate the demands for AI skills and jobs.
Recommended Citation
Kim, Jeongmin; Rai, Arun; and Lin, Yu-Kai, "AI Labor Markets: Toward a Dynamic Skills-Based Approach to Measurement" (2023). ICIS 2023 Proceedings. 6.
https://aisel.aisnet.org/icis2023/techandfow/techandfow/6
AI Labor Markets: Toward a Dynamic Skills-Based Approach to Measurement
Artificial intelligence (AI) is transforming the nature of work and reshaping labor markets. Viewing labor as a bundle of skills, recent research has analyzed AI skills and offered important insights about the impacts of AI on labor markets. We add to this on-going discourse and argue that taking a dynamic skill-based approach to measurement is critical: just like the development of AI is emergent and ever-evolving, so are AI skills. Taking stock of the literature, we show that existing studies tend to take a static approach to measuring AI skills, which fails to fully reflect the dynamic phenomenon of AI skills and could cause measurement errors. We propose a dynamic co-occurrence method and demonstrate that it performs better than the extant static methods, which can cause severe Type I and II errors, omit emerging AI skills, and temporally over- and under-estimate the demands for AI skills and jobs.
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